import argparse
import base64
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
import time
import uvicorn
from fastapi import FastAPI, File, UploadFile
# pip install python-multipart
app = FastAPI()
# weights, view_img, save_txt, imgsz = 1, 2, 3, 4
# save_dir = 1
# device, half = 1, 2
# model, stride = 1, 1
# names, colors = 1, 2
# save_img = 1
# opt = 1

# def initF(opt):
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str,
                    default='./weights/best.pt', help='model.pt path(s)')
# file/folder, 0 for webcam
parser.add_argument('--source', type=str,
                    default='data/images', help='source')
parser.add_argument('--img-size', type=int, default=640,
                    help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float,
                    default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float,
                    default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='0',
                    help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true',
                    help='display results')
parser.add_argument('--save-txt', action='store_true',
                    help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true',
                    help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int,
                    help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true',
                    help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true',
                    help='augmented inference')
parser.add_argument('--update', action='store_true',
                    help='update all models')
parser.add_argument('--project', default='runs/detect',
                    help='save results to project/name')
parser.add_argument('--name', default='exp',
                    help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true',
                    help='existing project/name ok, do not increment')
opt = parser.parse_args()
print(opt)
weights, view_img, save_txt, imgsz = opt.weights, opt.view_img, opt.save_txt, opt.img_size
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name,
                               exist_ok=opt.exist_ok))  # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True,
                                                      exist_ok=True)  # make dir

# Initialize
set_logging()
device = select_device(opt.device)

half = device.type != 'cpu'  # half precision only supported on CUDA

# Load model
model = attempt_load(weights, map_location=device)  # load FP32 model
stride = int(model.stride.max())  # model stride
imgsz = check_img_size(imgsz, s=stride)  # check img_size
if half:
    model.half()  # to FP16

    # Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
print(names)
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

# Set Dataloader
save_img = True

# Run inference

model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(
    next(model.parameters())))  # run once
# return opt


if __name__ == '__main__':

    # print(opt)
    check_requirements()
    uvicorn.run(app='main:app', host="0.0.0.0",
                port=8000, reload=False, debug=False)

    # with torch.no_grad():


def cv2_base64(image):
    base64_str = cv2.imencode('.jpg', image)[1].tostring()
    base64_str = base64.b64encode(base64_str)
    return base64_str

# 方法


def UseYolo(source):
    cudnn.benchmark = False  # set True to speed up constant image size inference
    dataset = LoadImages(source, img_size=imgsz, stride=stride)

    for path, img, im0s, vid_cap in dataset:
        t0 = time.time()
        print(device)
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(
            pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()

        # Process detections
        for i, det in enumerate(pred):  # detections per image

            p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + \
                ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            # normalization gain whwh
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
            lines = []
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    # add to string
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    # if save_txt:  # Write to file
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
                            gn).view(-1).tolist()  # normalized xywh
                    # label format
                    line = (names[int(cls)], *xywh)
                    # print(('%g ' * len(line)).rstrip() % line + '\n')
                    # with open(txt_path + '.txt', 'a') as f:
                    #     f.write(('%g ' * len(line)).rstrip() % line + '\n')
                    lines.append(line)
                    # if save_img or view_img:  # Add bbox to image
                    label = f'{names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, im0, label=label,
                                 color=colors[int(cls)], line_thickness=3)

                # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Save results (image with detections)
            # if save_img:
            #     if dataset.mode == 'image':
            #         cv2.imwrite(save_path, im0)
            print(line)
            return cv2_base64(im0), lines

    # if save_txt or save_img:
    #     s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
    #     print(f"Results saved to {save_dir}{s}")

    # print(f'Done. ({time.time() - t0:.3f}s)')


@app.post("/file_upload")
async def file_upload(file: UploadFile = File(...)):
    start = time.time()
    try:
        res = await file.read()
        # print(int(time.time()))
        with open(file.filename, "wb") as f:
            f.write(res)
            proPic, pos = UseYolo(file.filename)
            print(pos)
            # pos ?
            return {"success": True, 'processed': proPic, 'position': pos}
    except Exception as e:
        return {"success": False}
